Source code for flambe.model.logistic_regression

# type: ignore[override]

from typing import Optional, Tuple, Union

from torch import Tensor
from torch.nn import Sigmoid
from flambe.nn.module import Module  # type: ignore[attr-define]
from flambe.nn import MLPEncoder

[docs]class LogisticRegression(Module): """ Logistic regression model given an input vector v the forward calculation is sigmoid(Wv+b), where W is a weight vector and b a bias term. The result is then passed to a sigmoid function, which maps it as a real number in [0,1]. This is typically interpreted in classification settings as the probability of belonging to a given class. Attributes ---------- input_size : int Dimension (number of features) of the input vector. """ def __init__(self, input_size: int) -> None: """ Initialize the Logistic Regression Model. Parameters ---------- input_size: int The dimension of the input vector """ super().__init__() self.encoder = MLPEncoder(input_size, output_size=1, n_layers=1, output_activation=Sigmoid())
[docs] def forward(self, data: Tensor, target: Optional[Tensor] = None) -> Union[Tensor, Tuple[Tensor, Tensor]]: """Forward pass that encodes data Parameters ---------- data : Tensor input data to encode target: Optional[Tensor] target value, will be casted to a float tensor. """ encoding = self.encoder(data) return (encoding, target.float()) if target is not None else encoding